How Evan 15X'd TitanX's Customer Targeting

What does an elite AI-forward tech stack actually look like in practice?

AI is changing the toolset for every revenue creator. But with every tool now claiming to be "AI," you might feel like you're drowning.

To find what is truly worth your time, you need to learn from the leaders already doing it well.

High intent action over hearsay.

On Feb 18th, join Kieran Snaith, SVP of RevOps at Qualified, where he's spent 5 years handcrafting his own stack.

He will share how to evaluate what tools suit your team's needs best, the critical mistakes to avoid, and the 5 specific tools he is most excited about heading into 2026.

Stop being a bottleneck for your data and start building for true GTM autonomy!

Everyone's obsessed with intent signals.

Website visits. Content downloads. Third-party scraped activity.

And sure, they work. Kind of. Until you realize you're competing with 47 other vendors who got the same alert and are now flooding that prospect's inbox at the exact same moment.

Evan Dunn, VP of Marketing at TitanX, took a different approach.

Instead of chasing hot signals that everyone else sees, he went looking for something permanent. Something that doesn't change when the market gets noisy. Something that actually tells you who needs your product, not just who clicked on a competitor's blog post.

He used job descriptions.

And the results? Companies fitting his criteria are 15 times more likely to become customers. SDRs doubled their conversation-to-meeting rate. Some titles, like CROs, hit a 42% booking rate when all criteria aligned.

Let me walk you through how he did it.

The Problem

TitanX was detecting a pattern: lack of urgency in the prospects they were talking to.

People weren't feeling the pain of poor connect rates enough to act on it.

Evan's insight? Instead of trying to create urgency where it didn't exist, flip the script. Go find the teams that really, really, really need to fix their outbound calling motion.

Stop convincing. Start finding people who already have the problem.

The Playbook: Job Descriptions as ICP Signal

Here's the step-by-step framework Evan built:

Step 1: Define Your ICP and Identify the Language

Evan started by asking AI to define TitanX's ICP through deep research. He also took inputs from seasoned leaders on their team who know the space. Then he asked: what should a job description reflect if someone is an excellent fit? He pushed for synonyms, parallel definitions, and a broad understanding of language patterns that might appear in job descriptions.

This isn't about one keyword. It's about building a comprehensive map of how your ideal customers talk about the problems you solve.

Step 1: Pull Job Descriptions at Scale

Evan used Clay to pull job descriptions for five title sets across 28,000 accounts:

→SDRs
→AEs
→Sales leaders
→RevOps
→Demand gen / marketing (roles attached to revenue)

He used Stack (a Clay enrichment provider) because it pulls closed job descriptions, not just open roles. That means you can find the JD for someone who's been in-role for 9 months and reverse-engineer what they were hired to do.

Step 2: Run AI Analysis to Find Signal Patterns

Evan hooked up the Gemini API (cheaper than using credits in-platform) and ran AI agents to scan for three key signals:

1. Do they explicitly mention outbound in the job description?

2. Do they explicitly mention phone-based or cold calling activities?

3. Do they have AEs doing full-cycle (a proxy for sales intensity)?

This isn't about guessing. It's about finding language patterns that reveal internal priorities.

Step 3: Validate the Hypothesis with Your CRM Data

Here's where it gets good.

Evan pulled TitanX's entire customer dataset and ran mathematical analysis to see if the hypothesis held up. He asked: Do companies fitting these three criteria actually become customers more often?

The answer: 15 times more likely to be customers.

But here's the kicker: the ones in the pipeline currently were only 6.5 times more likely to fit these criteria.

That means there was a 9x delta to be achieved.

Translation? TitanX wasn't targeting their best-fit accounts in outbound. They were leaving a massive opportunity on the table. If they had been targeting correctly, the pipeline multiplier and customer multiplier would've been closer to each other.

Step 4: Test It in Real Outbound Motion

Evan's SDRs started calling into accounts where all three signals were true.

The results:

→8% conversation-to-meeting rate when the signals were false
→18% conversation-to-meeting rate when all three signals were true (doubling the rate)
→42% booking rate for CROs when all three signals aligned

Based on about 1,500-1,600 call results.

That's not incremental improvement. That's a fundamental shift in who you're talking to.

Why This Works Better Than Intent Signals

Intent signals tell you someone did something. Job descriptions tell you how the business is structured and what they prioritize.

As Evan put it: "A job description is intimate. It reveals the business: who they report to, how it's structured, what tech they use, what their first priorities are."

It's not some random person clicking around the web. It's a third-party signal that reflects internal reality.

And the best part? It doesn't decay in 48 hours. If someone was hired to build an outbound motion, that priority stays relevant for at least 12 months.

The Next Layer: Real-Time Signals on Top of Fit

Evan's not saying ignore intent signals. He's saying layer them on top of fit.

Once you know which of the 28,000 accounts actually need your product, then you can monitor for real-time signals:

→Website visits (excluding careers page, obviously)
→Newsletter engagement
→Deal room activity
→On-site de-anonymization

You're not chasing noise. You're watching the right people.

How to Build This for Your Business

Here's Evan's framework for any company:

1. Define your ICP with AI, then ask what job descriptions should reflect. Go deep. Get synonyms, parallel definitions, and broad language patterns that might appear in JDs.

2. Pull job descriptions at scale (use Clay + Their Stack or similar).

3. Run AI agents to scan job descriptions for the words and phrases you identified. Look for explicit mentions, not implications.

4. Validate with your CRM data. Are these accounts actually your best customers?

5. Test in outbound. Measure conversion rates. Refine.

The magic isn't in the tool. It's in the question you're asking.

Evan didn't ask "who's showing intent right now?"

He asked: "Who's structured in a way that mandates they care about our problem?"

That's the difference between chasing and creating.

If you want to stop competing for the same hot leads everyone else sees, this is how you do it.

Build your golden profile. Find the signal that doesn't decay. Then go have real conversations with people who actually need you.

That's what Revenue Creators do.

—Jared